m.tech embedded system amrita vishwa vidyapeetham, …

M.Tech Embedded System

Amrita Vishwa Vidyapeetham, Coimbatore

M.Tech Embedded Systems is a multidisciplinary programme which caters to diverse domains of

engineering starting from electronics, communication, electrical, instrumentation, computer science and

even mechanical with an exponential growth in job opportunities and research. It commenced as the

second PG programme of the EEE department. The course is well supported by a highly equipped

laboratory (Amrita Microsoft Embedded Systems Laboratory).

Amrita Microsoft Embedded Systems Laboratory was established in the year 2008 with the inception of

M. Tech (Embedded Systems) by the department of EEE in the year 2008. The lab can accommodate up

to forty students at a time. It was set up with financial support from MICROSOFT for half a crore. The

embedded systems lab has state of the art facilities with several hardware and software platforms for the

entire course.

The MTech. Embedded Systems curriculum is framed to cater to all the hardware boards by offering

various subjects like Real Time Systems, Embedded System Programming, Computer Architecture using

ARM, Digital Signal Processors, Artificial Intelligence, Embedded Systems Programming, IoT, Robotics,

Automotive in addition to the large number of elective courses offered.

Each subject has an associated lab which corroborates that practical training in the domain of Embedded

Systems. The Dissertation at the end of the program also helps in perpetuating that the laboratory is fully

utilized. The laboratory is open from 9 am to 10.30 pm to enable students to work on various boards and

update their skills.

With a strong focus on imparting research skills among the students, in significant areas of Embedded

Systems the program includes key components which would make the graduates suited and employable

in, industrial, government R&D, and academic settings, spanning diverse areas of the exponentially

growing embedded domain.

Program Educational Objectives (PEOs)

The educational objectives of the MTech Embedded Systems program include:

1. Graduates will acquire the ability to migrate to all domains of embedded solutions.

2. Graduates will practice ethics in their professional domain and imbibe the professional attitude

developed

3. Graduates will learn to work well in team environment considering the multidisciplinary aspect of

embedded systems.

Program Outcomes (POs):

On completion of the MTech (Embedded Systems) program, the graduate will develop:

PO1: An ability to independently carry out research/investigation and development work to solve

practical problems.

PO2: An ability to write and present a substantial technical report document.

PO3: Students should be able to demonstrate a degree of mastery over the area as per the specialization

of the program. The mastery should be at a level higher that the requirements in the appropriate

bachelor program.

PO4: Ability to acquire state-of-the-art technologies for development of embedded solutions.

PO5: Ability to work in a multidisciplinary environment employing fundamental knowledge in

embedded systems.

CURRICULUM

I Semester

Course

Code Type Subject L T P Credits

21MA610 FC Applied Mathematics for Embedded Systems 3-1-0 4

21ES601 FC Embedded System Programming 3-0-2 4

21ES602 FC Embedded System Design using ARM 3-0-2 4

21ES603 FC Signal & Image Processing 3-0-2 4

E Elective I 3-0-0 3

21ES681 SC Application Lab for Embedded Systems 0-0-2 1

21RM609 FC Research Methodology 2-0-0 2

21HU601 HU Amrita Values Program** P/F

21HU602 HU Career Competency I** P/F

Credits 22

II Semester

Course


21ES611 SC Real Time Operating Systems 3-0-2 4

21ES612 SC FPGA Based System Design 3-0-2 4

21ES613 SC Machine Learning for Embedded Applications 2-0-2 3

21ES614 SC Internet of Things 3-0-2 4

E Elective – II 3-0-0 3

E Elective – III 3-0-0 3

21HU603 HU Career Competency II 0-0-2 1

Credits 22

III Semester

Course


21ES798 P Dissertation I 10

Credits 10

IV Semester

Course


21ES799 P Dissertation II 16

Credits 16

Total Credits 70

List of Courses

Foundation Core

Course

Code Subject L T P Credits

21MA610 Applied Mathematics for Embedded Systems 3-1-0 4

21ES603 Signal and Image Processing 3-0-2 4

21ES601 Embedded System Programming 3-0-2 4

21ES602 Embedded System Design using ARM 3-0-2 4

21RM609 Research Methodology 2-0-0 2

Subject Core

Course

Code Subject L T P Credits

21ES681 Application Lab for Embedded Systems 0-0-2 1

21ES611 Real Time Operating Systems 3-0-2 4

21ES612 FPGA Based System Design 3-0-2 4

21ES613 Machine Learning for Embedded Applications 2-0-2 3

21ES614 Internet of Things 3-0-2 4

Electives

21ES631 Distributed Computing 3-0-0 3

21ES632 Hardware Software Co-Design 3-0-0 3

21ES633 Embedded System Design Process 2-0-2 3

21ES634 Embedded Systems for Automotive Applications 2-0-2 3

21ES635 Embedded Systems in Robotics 2-0-2 3

21ES636 Embedded Systems in Biomedical Applications 3-0-0 3

21ES637 Video Processing 3-0-0 3

21ES638 GPU Architecture and Programming 2-0-2 3

21ES639 Advanced Image Processing and Computer Vision 2-0-2 3

21ES640 Cryptography and Network Security 3-0-0 3

21ES641 Web Technologies and Applications 3-0-0 3

21ES642 Mobile Application Development 2-0-2 3

21ES643 Advanced Mobile and Wireless Networks 3-0-0 3

21ES644 Multi Core Architectures 2-0-2 3

21ES645 Fault Tolerant System 3-0-0 3

21ES646 Embedded Systems in Smart Grid 2-0-2 3

21ES647 Design for IoT and Cloud Computing 2-0-2 3

21ES648 Intelligent Systems Design 3-0-0 3

SYLLABUS

21MA610 APPLIED MATHEMATICS FOR EMBEDDED SYSTEMS 3-1-0-4

Linear Algebra: Review of Matrices. Vector spaces and subspaces, linear independence, basis and

dimensions, linear transformations, orthogonality, projections, and least square applications. Eigenvalues

and eigenvectors, Positive Definite Matrices - Minima, Maxima and saddle points, semidefinite and

indefinite matrices, Singular value decomposition.

Optimization: Least-squares and linear programming, convex and non-linear optimization. Convex sets,

convex optimization Problems, Optimization problem in standard form, Quasiconvex optimization, linear

optimization, quadratic optimization, inequality constraints, semi definite programming, vector

optimization. Duality, approximation and fitting, statistical estimation, geometric problems,

Unconstrained minimization, gradient descent method, steepest descent method, Newton’s method,

Equality constrained minimization, eliminating equality constraints, Newton’s method with equality

constraints, Interior point method.

TEXTBOOKS/REFERENCES:

1. Gilbert Strang, “Linear Algebra and Its Applications”, Fourth Edition, Cengage, 2006.

2. Howard Anton and Chris Rorres “Elementary Linear Algebra”, John Wiley & Sons, 1994, Seventh

Edition.

3. Edwin K.P. Chong, Stanislaw H. Zak, “An introduction to Optimization”, 2nd edition, Wiley, 2013.

4. Kalyanmoy Deb, “Optimization for Engineering Design: Algorithms and Examples”, Prentice Hall,

2002.

5. Stephen P. Boyd and Lieven Vandenberghe D, “Convex Optimization”, Cambridge University Press,

2004.

CO Code Course outcome statement

21MATXXX.1 Analyzing solvability of the linear system of equations and applying matrix

algebra in solving a system of linear Equations

21MATXXX.2 Understanding concepts of vector space and the link between linear

transformation and matrix.

21MATXXX.3 Understanding the concepts of Gram-Schmidt orthogonalization, Least squares

and Singular value decomposition.

21MATXXX.4 Understanding different types of Optimization Techniques in engineering

problems. Learning

21MATXXX.5 Understanding gradient based Optimizations Techniques in single variables as

well as multi-variables

21ES601 EMBEDDED SYSTEM PROGRAMMING 3-0-2-4

Version control system, benefits, Types of Version Control Systems, Centralized Version Control

Systems, Distributed Version Control Systems. Coding standard and guidelines. Code documentation.

Functions, Pointers, Structure, Data Structures- Stacks and Queues, Linked List.

Introduction to Object oriented design pattern. Basic elements, mutable and immutable type, tuples, list,

and dictionaries. Control statements, loops, Functions, modules, Exception and assertions. Classes,

Access Modifiers, dunder/magic methods, object-oriented programming, abstraction, inheritance,

encapsulation, polymorphism, Code testing.

Porting to microcontrollers, Code Analysis and Performance tuning


1. Jon Loeliger, Matthew McCullough,“Version Control with Git”, O'Reilly Media, Inc 2nd

Edition,2012

2. Zed A. Shaw, “Learn Python 3 the Hard Way”, Addison-Wesley, 2016

3. Robert Martins, “Clean Code” , Pearson Education, second edition,2012

4. Xavier Rival and Kwangkeun Yi , “Introduction to Static Analysis an Abstract Interpretation

Perspective”, MIT Press, January 2020


21ESXXX.1 Understand the basics of version control system and documentation.

21ESXXX.2 Develop structured programming using C.

21ESXXX.3 Develop code using object-oriented concepts.

21ESXXX.4 Analyze programs for real world applications.

CO Code PO1 PO2 PO3 PO4 PO5

21MATXXX.1 2 1

21MATXXX.2 1 1

21MATXXX.3 1 1

21MATXXX.4 1

21MATXXX.5 2 1


21ESXXX.1 1 2 2 2

21ESXXX.2 2 2 2 2

21ESXXX.3 2 2 3 2

21ESXXX.4 3 1 3 3 1

21ES602 EMBEDDED SYSTEM DESIGN USING ARM 3-0-2-4

An introduction to Embedded Processors – RISC verses CISC- CPU Performance Metrics –Benchmark -

RISC processor design. ARM Architecture – Programming Model, Pipelined data path design - Pipeline

Hazards, Addressing Modes, ARM Instruction set – Thumb2 Instruction - ARM Programming - Floating

Point data representation - Vector Floating Point Unit, Interrupts & Exception Handling- DSP Extensions

- Assembly programming. Memory system design - Memory Management unit - Cache Memory - Virtual

Memory. Introduction to ARM based Microcontrollers – Peripherals – Ports, Timers, PWM, ADC,

UART, SPI, I2C - Application development – Bare - metal Programming, Rapid Prototyping with

libraries - Case studies. ARM advanced CPU cores, Comparison with other architectures like PowerPC,

DSP, PIC, MSP, FPGA etc.


1. Steve Furber, “ARM System-on-Chip Architecture”, Pearson India, 2015.

2. Joseph Yiu, “The Definitive Guide to ARM® Cortex®-M3 and Cortex®-M4 Processors”, Third

Edition, Newnes, 2013.

3. Rob Toulson and Tim Wilmshurst, “Fast and Effective Embedded Systems Design: Applying the

ARM mbed”, Newnes, 2012.

4. Andrew Sloss, Dominic Symes and Chris Wright, “ARM System Developer's Guide: Designing and

Optimizing System Software”, Morgan Kaufumann Publisher, 2011. 5. ARM Microcontroller User Manual.


21ESXXX.1 Understand the architecture of ARM Processor.

21ESXXX.2 Analyze the instruction set of ARM Processor.

21ESXXX.3 Understand the interface of peripherals in ARM Microcontroller.

21ESXXX.4 Develop an Embedded application using ARM Microcontroller.


21ESXXX.1 1 2 3

21ESXXX.2 2 3 3

21ESXXX.3 2 2 3

21ESXXX.4 3 2 3 2 2

.

21ES603 SIGNAL AND IMAGE PROCESSING 3-0-2-4

Review of Frequency and time domain analysis -Discrete Fourier Transforms, Fast Fourier Transform.

Digital Filters-IIR Filters–Bilinear transformation. FIR filters– Windowing method- Application to real

time signals: simulated, audio with noise filtering and analysis of signals.

Elements of Visual Perception- Image Sensing and Acquisition-Simple Image Formation

- Image Sampling and Quantization—Image Quality-Introduction to colour image-, Introduction to color

image – RGB and HSI Models. Image transform: Fourier transform, DFT, Hadamard Transform. Filters-

Image enhancement in Spatial domain: Introduction to image enhancement, basic grey level transforms,

Histogram, Histogram-processing equalization, Matching & color histogram, Enhancement using

arithmetic/logic

Smoothening Spatial Filtering, Sharpening Spatial Filtering, Enhancement in spatial domain:

Algorithms in thresholding- Edge Detection, Hough Transform, Region based segmentation,

Morphological-dilation and erosion, opening and closing (Qualitatively). Shape Identification, Texture

Identification, Colour Identification Selection of sensors and Processors for real time implementation,

Applications in Real Time Scenario using Rasperry Pi Processor.

TEXTBOOKS/REFERENCES 1. Mitra S. K, “Digital Signal Processing, A Computer-Based Approach”, Third Edition,McGraw

Hill, 2005.

2. Ifeachor E. C and Jervis B. W, “Digital Signal Processing: A Practical Approach”,Second

Edition, Addison Wesley, 2002.

3. Steven W Smith, “The Scientist and Engineer’s Guide to DSP”, Newnes, 2002.

4. Rafael C. Gonzalez and Richard E. Woods, “Digital Image Processing”, Third Edition, Prentice Hall, 2008.

5. AshwinPajankar, “Raspberry Pi Computer Vision Programming”, Packt Publishing, May 2015.


21ESXXX.1 Understand discrete/ and fast fourier transforms for signal analysis.

21ESXXX.2 Comprehend digital filters for signal modification applications

21ESXXX.3 Understand image sensing techniques, image representations and image

transforms.

21ESXXX.4 Illustrate image enhancement techniques in spatial and frequency domain

on real time problems.

21ESXXX.5 Develop image processing techniques for embedded applications


21ESXXX.1 1 1 1 1

21ESXXX.2 2 1 1 1

21ESXXX.3 2 2 1 1

21ESXXX.4 2 2 1 1

21ESXXX.5 3 3 2 1 2

21ES681 APPLICATION LAB FOR EMBEDDED SYSTEM 0-0-2-1

Introduction to various software tools such as MATLAB/LabVIEW/Visual studio. Implement simple real-

world applications in embedded platforms.


21ESXXX.1 Comprehend the requirements of embedded system application.

21ESXXX.2 Analyse the feasibility of embedded system application.

21ESXXX.3 Develop prototype for embedded application.

21ESXXX.3 Validate the performance of embedded application.


21ESXXX.1 3 2 1 1

21ESXXX.2 3 2 1 1 1

21ESXXX.3 3 2 2 2 2

21ESXXX.4 3 2 2 2 2

21RM609 RESEARCH METHODOLOGY 2-0-0-2

Meaning of Research, Types of Research, Research Process, Problem definition, Objectives of Research,

Research Questions, Research design, Approaches to Research, Quantitative vs. Qualitative Approach,

Understanding Theory, Building and Validating Theoretical Models, Exploratory vs. Confirmatory

Research, Experimental vs Theoretical Research, Importance of reasoning in research.

Problem Formulation, Understanding Modeling & Simulation, Conducting Literature Review,

Referencing, Information Sources, Information Retrieval, Role of libraries in Information Retrieval, Tools

for identifying literatures, Indexing and abstracting services, Citation indexes

Experimental Research: Cause effect relationship, Development of Hypothesis, Measurement Systems

Analysis, Error Propagation, Validity of experiments, Statistical Design of Experiments, Field

Experiments, Data/Variable Types & Classification, Data collection, Numerical and Graphical Data

Analysis: Sampling, Observation, Surveys, Inferential Statistics, and Interpretation of Results

Preparation of Dissertation and Research Papers, Tables and illustrations, Guidelines for writing the

abstract, introduction, methodology, results and discussion, conclusion sections of a manuscript.

References, Citation and listing system of documents

Intellectual property rights (IPR) - patents-copyrights-Trademarks-Industrial design geographical

indication. Ethics of Research- Scientific Misconduct- Forms of Scientific Misconduct. Plagiarism,

Unscientific practices in thesis work, Ethics in science

TEXT BOOKS/ REFERENCES:

1. Bordens, K. S. and Abbott, B. B., “Research Design and Methods – A Process Approach”, 8th Edition,

McGraw-Hill, 2011

2. C. R. Kothari, “Research Methodology – Methods and Techniques”, 2nd Edition, New Age International

Publishers.

3. Davis, M., Davis K., and Dunagan M., “Scientific Papers and Presentations”, 3rd Edition, Elsevier Inc.

4. Michael P. Marder, “ Research Methods for Science”, Cambridge University Press, 2011

5. T. Ramappa, “Intellectual Property Rights Under WTO”, S. Chand, 2008

6. Robert P. Merges, Peter S. Menell, Mark A. Lemley, “Intellectual Property in New Technological

Age”. Aspen Law & Business; 6 edition July 2012


21ESXXX.1 Understand types and methods of research, modelling and referencing

21ESXXX.2 Analyse experimental results

21ESXXX.3 Prepare and present research papers

21ESXXX.4 Knowledge on IPR and ethics in publication


21ESXXX.1 1 3 1

21ESXXX.2 1 3 2

21ESXXX.3 2 3 2

21ESXXX.4 2 3 1

21ES611 REAL TIME OPERATING SYSTEM 3-0-2-4

Overview of concepts of GPOS, GPOS functionalities, Evolution of operating systems. Introduction to

real-time systems, RTOS basic architecture, RTOS vs. GPOS. Architecture of OS (Monolithic,

Microkernel, Layered, Exokernel and Hybrid kernel structures). POSIX Standards. RTOS Kernel

services.

Task Management -tasks, process and threads, task attributes and types, preemption-context switching,

task states and transition, task control block. Introduction to real-time task scheduling, clock-driven and

priority-driven scheduling, uniprocessor scheduling algorithms- RM-response time analysis, DM, EDF-

processor demand analysis, Least Laxity First (LLF), and introduction to multiprocessor scheduling

concepts. Blocking, deadlock, priority inversion and solutions.

Task Communication and Synchronization - Semaphores and Mutex, Mailbox, Queue, Pipes. Timer

Management, Interrupt handling, Memory Management-Cache and virtual memory, Input-Output

handling. Familiarization of FreeRTOS – architecture, porting, Real time applications using RTOS.


1. Jane W.S. Liu, “Real -Time Systems”, First Edition, Pearson Education, 2000.

2. Cheng, A. M. K., “Real-Time Systems: Scheduling, Analysis, and Verification”, First Edition, Wiley,

2002.

3. Krishna, C. M., Shin, K. G., “Real-Time Systems”, First Edition, McGraw-Hill, 2017.

4. Richard Barry, “Mastering the FreeRTOS™ Real Time Kernel A Hands-On Tutorial Guide”, First

Edition, Real Time Engineers Ltd., 2016.

5. Tanenbaum, “Modern Operating Systems,” Third Edition, Pearson Edition, 2009.


21ESXXX.1 Understand the basic concepts in real time systems.

21ESXXX.2 Illustrate various services provided by the RTOS Kernel.

21ESXXX.3 Analyze various real-time scheduling algorithms.

21ESXXX.4 Design and develop real time applications using RTOS.


21ESXXX.1 1 2

21ESXXX.2 1 1

21ESXXX.3 2 1 2 2

21ESXXX.4 3 2 3 3 2

21ES612 FPGA BASED SYSTEM DESIGN 3-0-2-4

Introduction to FPGAs – Design flow – Circuit Fabrics – LUTs and IO Blocks – FPGA Technology

overview – Digital Design for FPGAs - High Level System Architecture and Specification: Behavioural

modelling and simulation - Hardware description languages – Design of combinational and sequential

subsystems – Case Study of RTL Design for FPGAs – Interpreting Synthesis and Implementation reports

Design of data path and controller subsystems – FIFOs - Memory controllers – Platform FPGAs - DSP

blocks – FPGA Block RAMs - Synthesis issues – System Level synthesis from high level languages

Block-based design flow – Case study of block-based design of a digital system – FPGA processor fabrics

and bus interfaces – FPGA based embedded design flow

TEXTBOOKS/ REFERENCES

1. Michael D. Ciletti, “Advanced Digital Design with Verilog HDL”, Second Edition, Pearson

Higher Education, 2011.

2. Stephen Brown and Zvonko Vranesic, “Fundamental of Digital Logic with VHDL Design”, Third

Edition, McGraw Hill, 2009.

3. Samir Palnitkar, “Verilog HDL, A Guide to Digital Design and Synthesis”, Second Edition,

Pearson Education, 2003.

4. T. R. Padmanabhan and B. Bala Tripura Sundari, “Design Through Verilog HDL”, Wiley

Interscience, 2004

5. Wayne Wolf, “FPGA-Based System Design”, Prentice Hall India Pvt. Ltd., 2005.


21ESXXX.1 Understand synthesizable HDL modelling of digital subsystems.

21ESXXX.2 Formulate architecture of systems at the RTL abstraction.

21ESXXX.3 Implement digital systems in FPGA platforms and evaluate them based on tool

reports

21ESXXX.4 Employ custom and block design to realize embedded systems for FPGA

implementation.


21ESXXX.1 2 2 1

21ESXXX.2 2 2 1

21ESXXX.3 3 1 3 2 1

21ESXXX.4 3 2 3 2 2

21ES613 MACHINE LEARNING FOR EMBEDDED APPLICATIONS 2-0-2-3

Introduction to Machine learning, different forms of learning: supervised and unsupervised learning,

classification and regression, parametric and nonparametric models, curse of dimensionality, linear and

logistic regression. Support vector machines for regression and classification. Neural Networks,

Introduction to Convolutional Neural Network (CNN)

Motion Classification and Anomaly Detection. Edge Impulse, Data Collection, Feature Extraction from

Motion Data, Feature Selection in Edge Impulse, Machine Learning Pipeline, Model Training in Edge

Impulse, deploy a Trained Model to embedded platform, Anomaly Detection.

Audio classification and Keyword Spotting. Audio Data Capture, Audio Feature Extraction (MFCC),

Implementation Strategies and Sensor Fusion, CNN training for sound classification.

TEXTBOOKS / REFERENCES:

1. Trevor Hastie, Robert Tibshirani, Jerome Friedman, “The Elements of Statistical Learning: Data

Mining, Inference, and Prediction”, Second Edition, Springer, 2009.

2. Pete Warden, Daniel Situnayake, “TinyML: Machine Learning with TensorFlow Lite on Arduino and

Ultra-Low-Power Microcontrollers”, O'Reilly Media; 1st edition (16 December 2019)

3. Christopher M. Bishop, “Pattern Recognition and Machine Learning”, Springer, 2nd

edition 2011.

4. Tom M. Mitchell, “Machine Learning”, McGraw-Hill, 1st edition 1997.

https://www.coursera.org/learn/introduction-to-embedded-machine-learning#syllabus

https://sites.google.com/g.harvard.edu/tinyml/home

https://www.udemy.com/course/getting-started-with-embedded-ai-hands-on-experience/


21ESXXX.1 Understand the fundamentals of machine learning

21ESXXX.2 Familiarise supervised and unsupervised machine learning models.

21ESXXX.3 Implement trained ML model on an embedded platform

21ESXXX.4 Validate open-source deep learning model on an embedded platform


21ESXXX.1 1 1 1 1 1

https://www.coursera.org/learn/introduction-to-embedded-machine-learning#syllabus

https://sites.google.com/g.harvard.edu/tinyml/home

https://www.udemy.com/course/getting-started-with-embedded-ai-hands-on-experience/

21ESXXX.2 2 1 1 1

21ESXXX.3 2 2 3 3 2

21ESXXX.4 2 2 3 3 2

21ES614 INTERNET OF THINGS 3-0-2-4

Introduction to IoT - Definitions, frameworks and key technologies. Functional blocks of IoT systems:

hardware and software elements- devices, communications, services, management, security, and

application. Challenges to solve in IoT.

Basics of Networking & Sensor Networks - Applications, challenges - ISO/OSI Model, TCP/IP Model.

Sensor network architecture and design principles. IoT technology stack -overview of protocols in each

layer. Communication Protocols. Communication models, Application protocols for the transfer of sensor

data. Infrastructure for IoT: LoRa-Wan, 6LoWPAN, 5G and Sigfox. Operating systems and programming

environments for embedded units (Contiki).

Introduction to Cloud, Fog and Edge Computing- Modern trends in IoT – Industrial IoT, Wearable.

Applications of IoT - Smart Homes/Buildings, Smart Cities, Smart Industry, and Smart Medical care,

Smart Automation etc.


1. Andrew S. Tanenbaum and David J. Wetherall, “Computer Networks”, 5th Edition, Pearson

Education, 2011.

2. Holger Karl and Andreas Willig, “Protocols and Architectures for Wireless Sensor Networks”, John

Wiley and Sons Ltd., 2005.

3. Olivier Hersent, David Boswarthick and Omar Elloumi, “The Internet of Things: Key Applications

and Protocols”, Wiley, 2012.

4. Rayes, Ammar, Salam, Samer “Internet of Things from Hype to Reality” 2nd edition

5. Boris Adryan, Dominik Obermaier, Paul Fremantle “The Technical Foundations of IoT” Artech

House 2nd edition.


21ESXXX.1 Understand the concepts and principles of IoT.

21ESXXX.2 Implement communication protocols related to IoT and machine to machine

communication (M2M)

21ESXXX.3 Familiarize key technologies in an IoT framework.

21ESXXX.4 Develop IoT based solution for real world applications.


21ESXXX.1 1 1 3

21ESXXX.2 3 1 2 3

21ESXXX.3 1 1 3 2

21ESXXX.4 3 1 3 1 2

21ES631 DISTRIBUTED COMPUTING 3-0-0-3

Introduction to distributed computing systems (DCS), DCS design goals, Fundamental issues, System

architecture, Distributed and Centralized architecture. Distributed Coordination: Temporal ordering of

events, Lamport's logical clocks, Vector clocks; Ordering of messages, Physical clocks, Global state

detection, Process synchronization, Modelling of distributed real-time systems. Basics of real time

systems: Functional, Temporal and Dependability requirements.

Real time communication, Requirements of real time communication system, Flow Control-Explicit and

Implicit, Thrashing, Inter-process communication: Message passing communication, Remote procedure

call, Group communication, Deadlocks in distributed systems, Load scheduling and balancing techniques,

Consistency Models, Fault Tolerance.

Introduction to Distributed System Models, High-Performance Computing, Grid Computing, Cloud

Computing, Many-core Computing, Many-Task Computing, Data-Intensive Computing, Parallel

architectures, and Multithreaded programming. Introduction to GPU architecture and programming.

Usage of tools for GPU Programming.

TEXTBOOKS/REFERENCES: 1. G. Coulouris, J. Dollimore, T. Kindberg, G. Blair, “Distributed Systems: Concepts and Design,”

Addison Wesley, Fifth edition, 2012.

2. Kai Hwang Jack Dongarra Geoffrey Fox, “Distributed and Cloud Computing” From Parallel

Processing to the Internet of Things, 1st Edition, Morgan Kaufmann 2013.

3. Andrew S. Tanenbaum and Maarten van Steen. “Distributed Systems: Principles and Paradigms”

(DSPD), Prentice Hall, 2nd Edition, 2007

4. H Kopetz, “Real Time Systems: Design Principles for Distributed Embedded Applications”, Kluwer,

Edition 2, 2011.

5. Rajkumar Buyya, Satish Narayana Srirama, “Fog and Edge Computing: Principles and Paradigms”,

Wiley, 2019


21ESXXX.1 Understand the basics of distributed computing systems.

21ESXXX.2 Analyze the significance of time and various time synchronization methods in

distributed computing systems.

21ESXXX.3 Examine the significance and requirements of real time communication systems.

21ESXXX.4 Illustrate various distributed system models.


21ESXXX.1 1 1 2

21ESXXX.2 2 3 2

21ESXXX.3 2 3 2

21ESXXX.4 3 2 3 3 2

21ES632 HARDWARE SOFTWARE CO-DESIGN 3-0-0-3

Introduction to System Level Design –Generic Co-Design Methodology–Hardware-Software Co-Design

Models and Architectures –Languages for System Level Specification, Design and Modelling

Design Representation for System Level Synthesis –Models of Computation–Architectural, Selection–

Partitioning–Scheduling and Communication.

Hardware - Software Co-Simulation of Embedded Systems–Synthesis–Verification and Virtual

Prototyping - Implementation Case Studies – Performance Analysis and Optimization – Re-Targetable

Code Generation – FPGAs and Heterogeneous platforms


1. Patrick R. Schaumont, “A Practical Introduction to Hardware/Software Co-design” , Second

Edition, Springer, 2013.

2. Jorgen Staunstrup and Wayne Wolf, “Hardware/Software Co-design: Principle and Practice”,

Kluwer Academic Publishers, 1997.

3. Giovanni De Micheli, “Readings in Hardware Software Co-design “, Morgan Kaufmann,

Academic Press, 2002.

4. Daniel D. Gajski, Frank Vahid, Sanjiv Narayan, Jie Gong, “Specification and Design of

Embedded Systems”, Pearson Education publishing, 1994 edition, 2008 Impression

5. Vivado Design Suite User Guide: Embedded Processor Hardware Design UG898 (v2017.3)

October 27, 2017.


21ESXXX.1 Understand the need for hardware software co-design in the design flow process.

21ESXXX.2 Analyze hardware-software co-design problems for systems with moderate

complexity.

21ESXXX.3 Apply hardware-software co-design methods and techniques for embedded

systems.

21ESXXX.4 Apply different levels of abstractions and models for verification of embedded co-

design.


21ESXXX.1 1 2 1

21ESXXX.2 2 1 3 2

21ESXXX.3 3 1 3 2

21ESXXX.4 3 2 3 2 2

21ES633 EMBEDDED SYSTEM DESIGN PROCESS 2-0-2-3

Embedded system design cycle, Requirement analysis, planning, design, development, testing,

prototyping, deployment. Development methodologies, System modelling. - Modelling –Continuous

Dynamics, Discrete Systems, Hybrid Systems.

System components – Hardware - sensors & actuators, ADC, DAC, serial communication interfaces,

software- application and system software.

Error analysis, signal processing and conditioning for interfacing purposes: digital and analogue

quantities, noise reduction and digital filters.


1. D. Patranabis, “Sensors & Transducers”, Prentice Hall India,2005.

2. Ramón Pallás-Areny, John G. Webster, “Sensors and Signal Conditioning”, 2nd Edition WILEY,

2012.

3. Gerald D. Everett, Raymond McLeod, “Software Testing: Across the Entire Software Development

Life Cycle”, Wiley ,second edition

4. Roger S. Pressman, Bruce R. Maxim, “Software Engineering: a practitioner’s approach”, Boston,

Ninth edition.

5. Glenford J. Myers, Corey Sandler, Tom Badgett, “The Art of Software Testing,” Wiley ,3rd Edition.


21ESXXX.1 Understand the basics of embedded system development life cycle.

21ESXXX.2 Familiarize modelling of embedded system.

21ESXXX.3 Understand the operation of various sensors and actuators.

21ESXXX.4 Develop interfaces for sensors and actuators.


21ESXXX.1 1 1 1 3

21ESXXX.2 2 1 2 3

21ESXXX.3 1 1 1 3 2

21ESXXX.4 3 1 3 1 1

21ES634 EMBEDDED SYSTEMS FOR AUTOMOTIVE APPLICATIONS 2-0-2-3

Automotive Fundamentals – Vehicle functional domains and requirements – Automotive Electrical

subsystems- The systems approach to control and automotive instrumentation – Sensors and Actuators in

various vehicle domains. Systems in Power Train Electronics: Engine Management Systems in Chassis

control: ABS, ESP,TCS, Active Suspension Systems, Cruise Control and Adaptive Cruise control

systems – Body Electronic systems – Automotive Safety systems HVAC – Electric Hybrid Vehicles and

their configurations- Drive-by-wire systems – Autonomous and Connected Vehicles and their challenges-

Introduction to Embedded Automotive Protocols: CAN, LIN, Flex-Ray, MOST-AUTOSAR standard

and its applications - OSEK VDX Open Systems in Automotive Networks.


1. William B. Ribbens, “Understanding Automotive Electronics – An Engineering Perspective”, Eight

Edition, Elsevier Inc., 2017.

2. Robert Bosch GmbH, “Bosch Automotive Electrics and Automotive Electronics -Systems and

Components, Networking and Hybrid Drive”, Fifth Edition, Springer Vieweg, 2007.

3. Najamuz Zaman, “Automotive Electronics Design Fundamentals”, Springer, 2015.

4. V. A. W. Hillier and David R. Rogers, “Hillier’s Fundamentals of Motor Vehicle Technology on

Chassis and Body Electronics”, Fifth Edition, Nelson Thrones, 2007. 5. Tom Denton, “Automobile Electrical and Electronic Systems”, Fifth edition, Routledge, 2017.


21ESXXX.1 Understand various automotive subsystems

21ESXXX.2 Introduce Automotive sensors and actuators

21ESXXX.3 Develop automotive control systems in embedded platform.

21ESXXX.4 Understand various automotive communication protocols and software

architecture


21ESXXX.1 1 1

21ESXXX.2 1 1 1

21ESXXX.3 2 1 2 1 2

21ESXXX.4 2 1 2 1 2

21ES635 EMBEDDED SYSTEMS FOR ROBOTICS 2-0-2-3

Robots and Embedded Systems-Sensors, Microcontrollers and Actuators in Robots - Control - On-Off

Control, PID Control, Velocity Control and Position Control, Recent Trends in Robotics-

Milli/Micro/Nano Robot- Human-robot interaction.

Industrial Robots - Evolution of robotics, Robot anatomy, Manipulation and Control. Direct Kinematic

Model - Denavit-Hartenberg Notation, Kinematic Relationship between adjacent links, Manipulator

Transformation Matrix; Inverse Kinematic Model – Manipulator Workspace, Solvability, Solution

techniques, Closed form solution. Introduction to Robot dynamics & Control.

Mobile Robots, Concepts of Localization, and path planning. Autonomous robots- Swarm and

Collaborative robots. Robot Operating System: architecture, sensors, actuators and platforms supported.


1. Thomas Bräunl, “Embedded Robotics: Mobile Robot Design and Applications with Embedded

Systems”, Third Edition, Springer-Verlag Berlin Heidelberg, 2008.

2. R.K.Mittal and I.J.Nagrath, “Robotics and Control”, Tata McGraw-Hill Publishing Company Ltd.,

New Delhi, 2003.

3. John J. Craig, “Introduction to Robotics: Mechanics and Control”, Fourth Edition, Pearson, 2018.

4. Anis Koubaa, “Robot Operating System (ROS) The Complete Reference”, First Volume, Springer,

2016.

5. K.S. Fu, R.C. Gonzalez and C.S.G. Lee, “Robotics: Control, Sensing, Vision, and Intelligence”,

McGraw-Hill, New York, 1987.


21ESXXX.1 Understand the architecture and components of robotic systems.

21ESXXX.2 Develop the kinematic models of manipulators

21ESXXX.3 Develop the inverse kinematic models for manipulators.

21ESXXX.4 Implement algorithms in autonomous mobile robot path planning, localization and

control.


21ESXXX.1 1 1

21ESXXX.2 2 2 2 2

21ESXXX.3 2 2 2 2

21ESXXX.4 2 2 3 2

21ES636 EMBEDDED SYSTEMS IN BIOMEDICAL APPLICATIONS 3-0-0-3

Overview of biomedical devices – Origin of bio potentials – bio potential electrodes – bio potential

amplifiers, System Theory for Physiological Signals: Filters, Modeling – Embedded systems in Patient

monitoring: ECG, EEG, EMG, Blood pressure, respiration, pulse oximeters, diagnostic devices.

Non-invasive Diagnosis Using Sounds from Within the Body, Non-invasive Measurement of Blood

Pressure, Measurement of Electrical Potentials and Magnetic Fields from the Body Surface and

Plethysmography. Healthcare and the Wireless Sensor Network, Smart m-Health Sensing, m-Health and

Mobile Communication Systems, Data Collection and Decision Making. m-Health Computing m-Health

2.0, Social Networks, Health Apps, Cloud and Big Health Data, m-Health and Global Healthcare and the

Future of m-Health – case study.


1. John G. webster, Amit J. Nimunkar, “Medical Instrumentation - Application and Design”, Fifth

Edition, John Wiley and Sons, 2020.

2. Subhas Chandra Mukhopadhyay and Aime Lay-Ekuakille, “Advances in Biomedical Sensing,

Measurements, Instrumentation and Systems”, Springer, 2010.

3. Aime Lay-Ekuakille and Subhas Chandra Mukhopadhyay, “Wearable and Autonomous Biomedical

Devices and Systems for Smart Environment - Issues and Characterization”, Springer, 2010.

4. Robert B. Northrop, “Noninvasive Instrumentation and Measurement in Medical Diagnosis”, CRC

Press, 2019.

5. Roberts. H. Istepanian and Bryan Woodward, “m-Health Fundamentals and Applications”, Wiley,

2017.


21ESXXX.1 Understand the basics of Bio Potentials and Physiological Signals.

21ESXXX.2 Familiarise Patient Monitoring System using Embedded Systems.

21ESXXX.3 Study of Embedded Systems in Patient Assistive Devices.

21ESXXX.4 Analyse the application of Embedded systems in surgical devices, medical

imaging, clinical laboratory equipment etc.


21ESXXX.1 1

21ESXXX.2 1 1 1 1

21ESXXX.3 1 1 1 1

21ESXXX.4 1 1 1 1

21ES637 VIDEO PROCESSING 3-0-0-3

Introduction, Analog video and NTSC television, Spatio-temporal sampling; Sampling structure

conversion (without using motion), Motion Analysis, Real versus apparent motion, Spatial-temporal

constraint methods (optical flow equation), Block-matching methods, Mesh-based methods, Region-based

(parametric) motion modeling, Motion segmentation and layered video representations.

Motion-compensated (MC) filtering, Noise reduction, Signal recovery and general inverse problems,

Restoration (deblurring), Super resolution, Mosaicking, Deinterlacing, Frame-rate conversion (MC-

Interpolation).

Video Watermarking, Video Compression, Frame-based compression (principles behind MPEG-1/2),

Scalable or layered frame-based compression, Object-based compression (principles behind MPEG-4).

Surveillance - Video indexing, summarization, and retrieval, Object detection and tracking, Video

Analytics.


1. Y. Wang, J. Ostermann, and Y. Q. Zhang, “Video Processing and Communications”, Prentice Hall,

1st edition, 2002.

2. M. Tekalp, “Digital Video Processing”, Prentice Hall, 2nd

edition, 2005.

3. J. W. Woods, “Multidimensional signal, image and video processing and coding”, Academic Press /

Elsevier, 2nd

edition, 2012.

4. Linda G. Shapiro and George C. Stockman, “Computer Vision”,. Prentice-Hall, Inc., 1st edition, New

Jersey, 2001.


21ESXXX.1 Understand the attributes of video data.

21ESXXX.2 Familiarise the various motion analysis schemes.

21ESXXX.3 Develop applications for video restoration, super resolution, and Mosaicking.

21ESXXX.4 Apply video processing techniques for watermarking and compression.


21ESXXX.1 1 2

21ESXXX.2 1 1 2 1 1

21ESXXX.3 3 1 3 2 1

21ESXXX.4 3 2 3 2 1

21ES638 GPU ARCHITECTURE AND PROGRAMMING 2-0-2-3

Review of Traditional Computer Architecture – Basic five stage RISC Pipeline, Cache Memory, Register

File, SIMD instructions, GPU architectures - Streaming Multi Processors, Cache Hierarchy, The Graphics

Pipeline, Introduction to CUDA programming.

Multi-dimensional mapping of dataspace, Synchronization, Warp Scheduling, Divergence, Memory

Access Coalescing, Optimization examples: optimizing Reduction Kernels, Optimization examples:

Kernel Fusion, Thread and Block.

OpenCL basics, OpenCL for Heterogeneous Computing, Application Design: Efficient Neural Network

Training/Inferencing.


1. Jason Sanders and Edward Kandrot, “CUDA by Example: An Introduction to General-Purpose GPU

Programming”, Addison-Wesley, 1st edition, 2010

2. Benedict Gaster,Lee Howes, David R. Kaeli, “Heterogeneous Computing with OpenCL” Morgan

Kaufmann; 1st edition, 2011

3. David Kirk and Wen-meiHwu, “Programming Massively Parallel Processors”, Morgan Kaufmann,

3rd edition, 2010

4. John L.Hennessy and David A. Patterson, “Computer Architecture -- A Quantitative Approach”,

Morgan Kaufmann, 5th edition, 2011


21ESXXX.1 Understand the fundamentals of parallel programming.

21ESXXX.2 Familiarise the various OpenCL device architectures.

21ESXXX.3 Analyze OpenCL case studies.

21ESXXX.4 Develop an application using GPU.


21ESXXX.1 2 1 2 1

21ESXXX.2 2 1 2 1 1

21ESXXX.3 3 2 3 2 1

21ESXXX.4 3 2 3 3 2

21ES639 ADVANCED IMAGE PROCESSING AND COMPUTER VISION 2-0-2-3

Review of Image Processing - Image Formation, Capture and Representation, Linear Filtering,

Correlation, Convolution. Visual Features and Representations: Edge Detection, Object Boundary and

Shape Representations, Interest or Corner Point Detectors, Histogram of Oriented Gradients, Scale

Invariant Feature Transform, Speeded up Robust Features, Saliency. Visual Matching: Bag-of-words,

VLAD; RANSAC, Hough transform; Pyramid Matching; Optical Flow

Basics of Artificial Neural Network for Pattern Classification, Convolutional Neural Networks

Applications – Medical Image Segmentation, Motion Estimation and Object Tracking, Face and Facial

Expression Recognition, Image Fusion, Gesture Recognition, Remote sensing etc.


1. Ralph Gonzalez, Richard Woods, Steven Eddins, “Digital Image Processing Using MATLAB”,

McGraw Hill Education, 2nd

edition, 2017.

2. Richard Szeliski ,“Computer Vision: Algorithms and Applications”, Springer, 2010.

3. Laurene Fausett, “Fundamentals of Neural Networks Architectures, Algorithms and Applications”,

Pearson, 1st edition, 2004

4. Forsyth & Ponce, “Computer Vision-A Modern Approach,” Pearson Education, 2nd

edition 2015.

5. M.K. Bhuyan, “Computer Vision and Image Processing: Fundamentals and Applications”, CRC

Press, 1st edition, 2019


21ESXXX.1 Familiarise the fundamentals of image processing.

21ESXXX.2 Understand neural networks for Image classification problems.

21ESXXX.3 Apply advanced image processing techniques.

21ESXXX.4 Design solutions for real-world image processing problems.


21ESXXX.1 1 1 1 1

21ESXXX.2 2 1 1 1 1

21ESXXX.3 2 2 2 2 2

21ESXXX.4 3 2 3 2 2

21ES640 CRYPTOGRAPHY AND NETWORK SECURITY 3-0-0-3

Classical Encryption Techniques – Symmetric Cipher Model – Steganography – AES Cipher -Symmetric

Cipher – Multiple Encryption and triple DES – Blocks Cipher – stream Cipher – Confidentiality using

symmetric encryption – Placement of encryption function – random number generation – Introduction to

number theory – Cryptosystems – message authentication and Hash functions – requirements – functions

– course – Hash and MAC algorithms – secure Hash algorithms – Digital signatures and authentication

protocols – standard – authentication applications – overview architecture – web security - socket layer

and transport layer security – Intruders – Detection – Malicious software – viruses and related threats –

denial of service - counter measures – firewalls – design principles – trusted systems - firmware security -

IoT security.


1. William Stallings, “Cryptography and Network Security – Principles and Practices”, Seventh

Edition, Prentice Hall, 2017.

2. Douglas R Stinson, “Cryptography: Theory and Practice”, Fourth Edition, Chapman and Hall/CRC,

2018.

3. Arshdeep Bahga, Vijay Madisetti, “Internet of Things – A hands-on approach”, Universities Press,

2015.

4. Mark Ciampa, “Security+ Guide to Network Security Fundamentals”, Fifth Edition, Cengage

Learning, 2014.


21ESXXX.1 Understand various encryption techniques.

21ESXXX.2 Understand the number theory in cryptographic schemes.

21ESXXX.3 Illustrate various authentication protocols.

21ESXXX.4 Analyse various software threats and counter measures.


21ESXXX.1 1 1 1

21ESXXX.2 2 1 1

21ESXXX.3 2 1 1 1 2

21ESXXX.4 2 2 2 1 2

21ES641 WEB TECHNOLOGIES AND APPLICATIONS 3-0-0-3

Web essentials: Creating a website – Working principle of a website – Browser fundamentals – Authoring

tools – Types of servers: Application Server – Web Server – Database Server; Scripting essentials: Need

for Scripting languages – Types of scripting languages – Client-side scripting

Server-side scripting – PHP – Working principle of PHP – PHP Variables – Constants – Operators – Flow

Control and Looping – Arrays – Strings – Functions – File Handling – PHP and MySQL – PHP and

HTML – Cookies – Simple PHP scripts. XML-Documents and Vocabularies-Versions and Declaration-

Namespaces- DOM based XML processing Event-oriented Parsing: XML-Documents and Vocabularies-

Versions and Declaration - Namespaces - DOM based XML processing Event-oriented Parsing

Application essentials: Creation of simple interactive applications – Simple database applications –

Multimedia applications – Design and development of information systems – Personal Information

System – Information retrieval system – Social networking applications.


1. Robin Nixon, “Learning PHP, MySQL, JavaScript, CSS & HTML5”, Fifth Edition, O’REILLY,

2018.

2. Jeffrey C. Jackson, “Web Technologies--A Computer Science Perspective”, Pearson Education, 2006.

3. Robert. W. Sebesta, “Programming the World Wide Web”, Eighth Edition, Pearson Education, 2015.

4. Bates, “Web Programming: Building Internet Applications”, Third Edition, Wiley, 2010.

5. R. Kelly Rainer, Casey G. Cegielski, Brad Prince, “Introduction to Information Systems”, Eighth

Edition, Wiley Publication, 2019.


21ESXXX.1 Comprehend the concepts of responsive web design.

21ESXXX.2 Apply markup and scripting languages to design and validate dynamic web pages.

21ESXXX.3 Evaluate the appropriateness of client/server applications.

21ESXXX.4 Develop client/server applications with database.


21ESXXX.1 1 1

21ESXXX.2 2 1 1

21ESXXX.3 2 1 1 1 2

21ESXXX.4 2 1 1 1 2

21ES642 MOBILE APPLICATION DEVELOPMENT 2-0-2-3

Introduction to mobile application development platforms, Application Development-Layouts, Views,

Resources, Activities, Intents, Background tasks, Connecting to the Internet, Fragments, Preferences.

User Interaction – input, menu items, custom views, User Experience – themes and styles, lists and

adapters, material design, adaptive layouts, accessibility, localization, debugging the UI Storing Data,

SQLite database, Sharing Data, content resolvers and providers, loaders to load data.

Services, background work, alarms, broadcast receivers, Notification, widgets, location-based services

and Google maps. transferring data efficiently, publishing app, Multiple form factors, sensors, Google

cloud messaging, monetizing mobile app.


1. Phillips, Stewart, Hardy and Marsicano, “Android Programming (Big Nerd Ranch Guide)”, Fourth

Edition, Big Nerd Ranch Guides, 2019.

2. Hellman, “Android Programming – Pushing the limits”, First Edition, Wiley, 2013.

3. Tejinder Randhawa, “Mobile Applications Design, Development and Optimization”, Springer

International Publishing, 2021.

4. Joseph Annuzzi Jr., Lauren Darcey, and Shane Conder, “Advanced Android Application

Development”, Fourth Edition, Addison-Wesley Professional, 2014.


21ESXXX.1 Understand Android programming.

21ESXXX.2 Develop Android programs.

21ESXXX.3 Develop mobile applications with cloud services.

21ESXXX.4 Analyse various services of mobile applications development.


21ESXXX.1 1 1

21ESXXX.2 2 1 2 2

21ESXXX.3 2 1 1 2 2

21ESXXX.4 2 1 1 2 2

21ES643 ADVANCED MOBILE AND WIRELESS NETWORKS 3-0-0-3

Wireless networking - Physical layer- OFDM and 802.11 PHY, Multi-antenna systems and MIMO,

Overview of 802.11n/ac PHY. MAC layer- CSMA/CA and Wi-Fi MAC overview, Wide bandwidth

channel access techniques (802.11n/ac), Energy efficiency and rate control.

Multi-gigabit wireless networks - GSM, 3G/4G, UMTS, CDMA, HSDPA, QoS Management. Next

generation (5G) wireless technologies- Upper Gigahertz and Terahertz wireless communications -

Millimeter wave networking, Directionality and beam forming, Mobility and signal blockage, IEEE

802.11ad (60 GHz WLAN) MAC and PHY overview. Visible light communication - High-speed

networking using LEDs, IEEE 802.15.7 PHY and MAC overview. Sensing through visible light, Visible

light indoor localization and positioning, WiFi fingerprinting - protocols and challenges, Non-WiFi

localization.

Future mobile networks - Drone networking- multi-UAV networks, architectures and civilian

applications, Communication challenges and protocols for micro-UAVs. Connected and autonomous

cars- Wireless technologies for Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V)

communications, Automotive surrounding sensing with GHz and THz signals.


1. Theodore S. Rappaport, “Wireless Communications: Principles and Practice”, Second Edition,

Pearson Education, 2009.

2. Matthew Gast, “802.11n/ac: A Survival Guide”, First Edition, O'Reilly Media, 2013.

3. Pei Zheng Larry Peterson Bruce Davie Adrian Farrel, “Wireless Networking Complete”, First

Edition, Morgan Kaufmann, 2009.

4. William Lee, “Wireless and Cellular Telecommunications”, Third Edition, McGraw Hill, 2006.

5. Saad Asif, “5G Mobile Communications Concepts and Technologies”, First Edition, CRC Press,

2018.


21ESXXX.1 Understand the capabilities of WiFi based communication systems.

21ESXXX.2 Comprehend next generation 5G communication networks.

21ESXXX.3 Familiarise different localization techniques in communication networks.

21ESXXX.4 Design wireless networks for real world applications.


21ESXXX.1 1 1

21ESXXX.2 1 2 2

21ESXXX.3 1 2 2

21ESXXX.4 2 1 1 2 2

21ES644 MULTI CORE ARCHITECTURES 2-0-2-3

Review of Computer Design - Measuring performance Instruction level parallelism - Branch prediction

techniques - Static & Dynamic scheduling – Speculation - Limits of ILP. Thread-level parallelism, Multi-

issue, and Multi-core processors – Homogenous and Heterogenous multicore systems

Shared and Distributed memory -Transaction Memory issues Memory hierarchy design - Cache

coherence, Memory wall problem - Advanced Cache Memory design - Virtual Memory, Storage Systems

- Ware-house Scale Computers

Power optimization- Dynamic Voltage Frequency Scaling - Multi-core architectures for embedded

systems – Fault Tolerant aspects for multi core systems- Programming environments for multi-core.


1. Peter S. Pacheco, “An Introduction to Parallel Programming,” Morgan Kauffman/Elsevier,

2011. 2. Yan Solihin, “Fundamentals of Parallel Multicore Architecture”, CRC Press, 2016. 3. Georgios Kornaros, “Multi-core Embedded Systems”, CRC Press, Taylor and Francis Group, First

edition, 2019. 4. Victor Alessandrini, Morgan Kaufmann, “Shared Memory Application Programming, Concepts

and Strategies in Multicore Application Programming”, 1st Edition 2015.

5. Darryl Gove, “Multicore Application Programming for Windows, Linux, and Oracle Solaris”,

Pearson, 2011.


21ESXXX.1 Understand instruction level and thread level parallelism and branch prediction

techniques.

21ESXXX.2 Develop static and dynamic scheduling algorithms.

21ESXXX.3 Analyse memory hierarchy design and cache coherency problem.

21ESXXX.4 Discuss concepts on multi-issue and multi-core processors with power

optimization.


21ESXXX.1 1 1

21ESXXX.2 2 1 2 2

21ESXXX.3 2 1 1 2 2

21ESXXX.4 2 1 1 2 2

21ES645 FAULT TOLERANT SYSTEM 3-0-0-3

Goals and Applications of Fault Tolerant Computing - Reliability, Availability, Safety, Dependability,

Long Life, Critical Computation, High Availability Applications, Fault Tolerance as a Design Objective.

Fault Models - Faults, Errors, and Failures, Causes and Characteristics of Faults, Logical and Physical

Faults, Error Models.

Fault Tolerant Design Techniques: Hardware redundancy, Software Redundancy, Time redundancy and

Information redundancy. Check pointing, Fault tolerant networks, Reconfiguration-based fault tolerance.

Reliability Evaluation Techniques - Failure Rate, Mean Time to Repair, Mean Time Between Failure,

Reliability Modelling, Fault Coverage, M-of-N Systems, Markov Models, Safety, Maintainability,

Availability. Case studies of fault tolerant systems and current research issues - Space Shuttle, Tandem 16

Non-Stop System, Recovery oriented computing, Fault tolerant platforms for Automotive Safety-Critical,

Reliability and Fault tolerance in Collective Robot Systems.


1. Israel Koren and C. Mani Krishna, “Fault Tolerant Systems”, Elsevier, 2nd

edition, 2020.

2. D. K. Pradhan, “Fault-Tolerant Computing, Theory and Techniques”, Prentice-Hall, 1998

3. M. L. Shooman, “Reliability of Computer Systems and Networks Fault Tolerance Analysis and

Design,” Wiley, 2003

4. Elena Dubrova, “Fault-Tolerant Design,” Springer-Verlag New York, 2013.

5. Barry W. Johnson, “Design and Analysis of Fault-Tolerant Digital System”, Addison, 2009.


21ESXXX.1 Understand basics of fault tolerance and fault models

21ESXXX.2 Discuss various forms of redundancies and fault tolerant design techniques

21ESXXX.3 Develop concepts on system reliability

21ESXXX.4 Comprehend different fault tolerant design concepts.


21ESXXX.1 1 1

21ESXXX.2 2 1 2 2

21ESXXX.3 2 1 1 2 2

21ESXXX.4 2 1 1 2 2

21ES646 EMBEDDED SYSTEMS IN SMART GRID 2-0-2-3

Smart grid definition. Smart grid vs conventional grid. Smart Grid technologies- Power system and ICT in

Generation, Transmission and Distribution. Basic understanding of power systems. Management aspects

(Utility, Operator, Consumer). Evolution of automation in power system. Smart Grid features- Distributed

generation, storage, DD, DR, AMI, WAMS, WACS). Sensors - CT, PT; Embedded Devices - IED, PMU,

PDC, CT, PT, relays, DR Switch; Algorithms; Communication- Standards, Technology, and protocols.

IoT applications in power system – Case study 1 generation control, load management, dynamic pricing

etc.; IoT for domestic prosumers. Case Study 2 -Smart microgrid simulator (SMGS), DR, DD, Energy

storage, Communication.


1. James Momoh, “Smart Grid: Fundamentals of Design and Analysis”, Wiley. IEEE Press, March

2012.

2. Janaka Ekanayake, Nick Jenkins, Kithsiri Liyanage, Jianzhong Wu and Akihiko Yokoyama, Smart

Grid: Technology and Applications”, Wiley, February 2012.

3. Nouredine Hadjsaïd and Jean Claude Sabonnadière, “Smart Grids”, Wiley ISTE, May 2012.

4. Ali Keyhani and Muhammad Marwali, “Smart Power Grids”, Springer, 2011.

5. Vijay Madisetti and Arshdeep Bahga, “Internet of Things: A Hands-on Approach”, Hardcover –

Import, 2014.


21ESXXX.1 Understanding the basics of power system management and its automation.

21ESXXX.2 Explore the features of Smart grid.

21ESXXX.3 Learn different Sensors and embedded devices used in smart grid.

21ESXXX.4 Examine the different communication standards, technologies and protocols for

smart grid.

21ESXXX.5 Investigate the IoT applications in Smart grid.


21ESXXX.1 1 1

21ESXXX.2 2 1 2 2

21ESXXX.3 2 1 1 2 2

21ESXXX.4 2 1 1 2 2

21ES647 DESIGN FOR IoT AND CLOUD COMPUTING 2-0-2-3

Embedded Systems: Rise of embedded systems and their transition to intelligent systems and to Internet

of Things -RFIDs, NFC, Web of Things - Embedded Systems Design: power and energy consumption;

hardware design elements, software platforms –OS and applications, code optimization, validation and

robust code generation; system integration, debugging and test methodology; tools for coding, debugging,

optimization, and documentation; measurement of system performance, Creating virtual prototypes -

hardware software emulation. IoT Reference Architectures, Introduction to Node Red, Visual Prototyping

with Arduino and connectivity to IoT platforms, Applications: Healthcare and home automation

examples. Cloud Computing: Infrastructure as a Service (IaaS), Cloud Database, Cloud storage. Platform

as a Service (PaaS) for Web Rapid Application Development (RAD), Distributed Storage, Distributed

Computing frameworks. Connectivity to remote server database, data access-storage processing.

Development of cloud server and web applications.


1. Barry, P., and Crowley, P., “Modern Embedded Computing”, Morgan Kaufmann, 2012.

2. Vijay Madisetti and Arshdeep Bahga, “Internet of Things: A Hands-on Approach”, Hardcover

Import, 2014.

3. Thomas Erl, “Cloud Computing: Concepts, Technology & Architecture”, Prentice Hall, May 2013.

4. Michael J. Kavis, “Architecting the Cloud: Design Decisions for Cloud Computing Service

Models (SaaS, PaaS, &IaaS)”, Wiley CIO Series, January 2014.

5. George Reese, “Cloud Application Architectures: Building Applications and Infrastructure in the

Cloud”, O'Reilly, 2009.


21ESXXX.1 Understand the challenges and requirement of IoT framework.

21ESXXX.2 Distinguish applications from ubiquitous computing, IoT and WoT.

21ESXXX.3 Discuss the issues in system integration, debugging, testing and analysing the

system performance.

21ESXXX.4 Design an IoT application.


21ESXXX.1 1 1

21ESXXX.2 2 1 2 2

21ESXXX.3 2 1 1 2 2

21ESXXX.4 2 1 1 2 2

21ES648 INTELLIGENT SYSTEMS DESIGN 3-0-0 -3

Intelligent Systems – Definition, Need and role of embedded engineers. Architectures of intelligent

systems – Automation, sensors, artificial intelligence techniques, actuators. Intelligent agents – definition,

applications of intelligent agents in science, technology, business and commercial. Supporting

technologies - Data mining, knowledge management, control and automation. Types of systems – Expert

Systems, Recommendation Systems, Cognitive Systems, Swarm intelligent systems, Hybrid computing

systems. Behaviour Oriented Design (BOD) – Definition, Extension to object-oriented design, process of

specifying agency, specifying the agent's priorities using POSH dynamic plans.

TEXTBOOKS/REFERENCE BOOKS:

1. Karray F, Karray FO, De Silva CW, “Soft computing and intelligent systems design: theory, tools,

and applications”. Pearson Education; 2009.

2. Larry Bielawski, Robert Lewand, “Intelligent Systems Design: Integrating Expert Systems,

Hypermedia, and Database Technologies”, First Edition, Wiley Professional Computing.

3. Alexander M. Meystel, James S. Albus, “Intelligent Systems: Architecture, Design, and Control”,

First Edition, Wiley – Interscience Publication, 2002.

4. Anindita Das, “Artificial Intelligence and Soft Computing for Beginners”, 2nd Edition, Arizona

Business Alliance, 2014

CO Code Course Outcome Statement

21ESXXX.1 Understand the need and scope of intelligent systems and the role of embedded

engineers in the design of such systems.

21ESXXX.2 Familiarize the components of intelligent systems especially intelligent agents and

its applications.

21ESXXX.3 Analyze the various types of intelligent systems.

21ESXXX.4 Understand Behaviour Oriented Design and process of defining agents.

21ESXXXX

DISSERTATION

0-0-0-10

The students are made to choose a suitable

problem, comprehend and analyse the problem

after detailed literature survey.


21ESXXX.1 Identify a topic based on recent literature in embedded systems.

21ESXXX.2 Formulate the framework for implementation.

21ESXXX.3 Choose computational and analytical tools for implementation

21ESXXX.4 Communicate technical content orally and document the findings.


21ESXXX.1 1 1 1 1

21ESXXX.2 2 2 1 1

21ESXXX.3 2 2 3 2 2


21ESXXX.1 1 2 3 1

21ESXXX.2 2 1 2 3 1

21ESXXX.3 1 1 2 1

21ESXXX.4 2 1 2 3 2

21ESXXX.4 2 3 2

21ES799 DISSERTATION II 0-0-0-16

The students are made to work on the problem selected and comprehend and analyse the results.


21ESXXX.1 Plan the project implementation with embedded system domain knowledge

21ESXXX.2 Implementation of project methodology in software/hardware aspects

21ESXXX.3 Analyse the results and perform comparative analysis with existing frameworks.

21ESXXX.4 Prepare technical reports, research papers, and disseminate knowledge


21ESXXX.1 1 2 2 2 2

21ESXXX.2 2 2 2 2 2

21ESXXX.3 2 2 2 2 2

21ESXXX.4 2 3 2 3 3